Eslami Touraj, Steinberger Martin, Csizmazia Christian, Jungbauer Alois, Lingg Nico
Department of Biotechnology, Institute of Bioprocess Science and Engineering, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, Vienna A-1190, Austria; Evon GmbH, Wollsdorf 154, A-8181St., Ruprecht an der Raab, Austria.
Institute of Automation and Control, Graz University of Technology, Inffeldgasse 21b, Graz A-8010, Austria.
J Chromatogr A. 2022 Sep 13;1680:463420. doi: 10.1016/j.chroma.2022.463420. Epub 2022 Aug 13.
In preparative and industrial chromatography, the current viewpoint is that the dynamic binding capacity governs the process economy, and increased dynamic binding capacity and column utilization are achieved at the expense of productivity. The dynamic binding capacity in chromatography increases with residence time until it reaches a plateau, whereas productivity has an optimum. Therefore, the loading step of a chromatographic process is a balancing act between productivity, column utilization, and buffer consumption. This work presents an online optimization approach for capture chromatography that employs a residence time gradient during the loading step to improve the traditional trade-off between productivity and resin utilization. The approach uses the extended Kalman filter as a soft sensor for product concentration in the system and a model predictive controller to accomplish online optimization using the pore diffusion model as a simple mechanistic model. When a soft sensor for the product is placed before and after the column, the model predictive controller can forecast the optimal condition to maximize productivity and resin utilization. The controller can also account for varying feed concentrations. This study examined the robustness as the feed concentration varied within a range of 50%. The online optimization was demonstrated with two model systems: purification of a monoclonal antibody by protein A affinity and lysozyme by cation-exchange chromatography. Using the presented optimization strategy with a controller saves up to 43% of the buffer and increases the productivity together with resin utilization in a similar range as a multi-column continuous counter-current loading process.
在制备色谱和工业色谱中，当前的观点是动态结合容量决定了过程经济性，而提高动态结合容量和柱利用率是以牺牲生产率为代价的。色谱中的动态结合容量随停留时间增加，直至达到平稳状态，而生产率存在一个最佳值。因此，色谱过程的上样步骤是生产率、柱利用率和缓冲液消耗之间的平衡行为。这项工作提出了一种用于捕获色谱的在线优化方法，该方法在加载步骤中采用停留时间梯度，以改善生产率和树脂利用率之间的传统权衡。该方法使用扩展卡尔曼滤波器作为系统中产物浓度的软传感器，并使用模型预测控制器，以孔扩散模型作为简单的机理模型来完成在线优化。当在柱前和柱后放置产物的软传感器时，模型预测控制器可以预测最佳条件，以最大化生产率和树脂利用率。该控制器还可以考虑进料浓度的变化。本研究考察了进料浓度在50%范围内变化时的稳健性。通过两个模型系统展示了在线优化：用蛋白A亲和法纯化单克隆抗体和用阳离子交换色谱法纯化溶菌酶。使用带有控制器的优化策略可节省高达43%的缓冲液，并在与多柱连续逆流加载过程类似的范围内提高生产率和树脂利用率。
